RT-ODLab/models/detectors/yolov8/yolov8_neck.py

import torch
import torch.nn as nn

try:
    from .yolov8_basic import Conv
except:
    from yolov8_basic import Conv


# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
class SPPF(nn.Module):
    """
        This code referenced to https://github.com/ultralytics/yolov5
    """
    def __init__(self, cfg, in_dim, out_dim, expand_ratio=0.5):
        super().__init__()
        inter_dim = int(in_dim * expand_ratio)
        self.out_dim = out_dim
        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
        self.cv2 = Conv(inter_dim * 4, out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
        self.m = nn.MaxPool2d(kernel_size=cfg['pooling_size'], stride=1, padding=cfg['pooling_size'] // 2)

    def forward(self, x):
        x = self.cv1(x)
        y1 = self.m(x)
        y2 = self.m(y1)

        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))


# SPPF block with CSP module
class SPPFBlockCSP(nn.Module):
    """
        CSP Spatial Pyramid Pooling Block
    """
    def __init__(self, cfg, in_dim, out_dim, expand_ratio):
        super(SPPFBlockCSP, self).__init__()
        inter_dim = int(in_dim * expand_ratio)
        self.out_dim = out_dim
        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
        self.cv2 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
        self.m = nn.Sequential(
            Conv(inter_dim, inter_dim, k=3, p=1, 
                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
                 depthwise=cfg['neck_depthwise']),
            SPPF(cfg, inter_dim, inter_dim, expand_ratio=1.0),
            Conv(inter_dim, inter_dim, k=3, p=1, 
                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
                 depthwise=cfg['neck_depthwise'])
        )
        self.cv3 = Conv(inter_dim * 2, self.out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])

        
    def forward(self, x):
        x1 = self.cv1(x)
        x2 = self.cv2(x)
        x3 = self.m(x2)
        y = self.cv3(torch.cat([x1, x3], dim=1))

        return y


def build_neck(cfg, in_dim, out_dim):
    model = cfg['neck']
    print('==============================')
    print('Neck: {}'.format(model))
    # build neck
    if model == 'sppf':
        neck = SPPF(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])
    elif model == 'csp_sppf':
        neck = SPPFBlockCSP(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])

    return neck


if __name__ == '__main__':
    import time
    from thop import profile
    cfg = {
        ## Neck: SPP
        'neck': 'sppf',
        'neck_expand_ratio': 0.5,
        'pooling_size': 5,
        'neck_act': 'silu',
        'neck_norm': 'BN',
        'neck_depthwise': False,
    }
    in_dim = 512
    out_dim = 512
    # Head-1
    model = build_neck(cfg, in_dim, out_dim)
    feat = torch.randn(1, in_dim, 20, 20)
    t0 = time.time()
    outputs = model(feat)
    t1 = time.time()
    print('Time: ', t1 - t0)
    # for out in outputs:
    #     print(out.shape)

    print('==============================')
    flops, params = profile(model, inputs=(feat, ), verbose=False)
    print('==============================')
    print('FPN: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
    print('FPN: Params : {:.2f} M'.format(params / 1e6))